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A Sociotechnical Systems Framework for the Application of Artificial Intelligence in Health Care Delivery
Open accessAbstract
In the coming years, artificial intelligence (AI) will pervade almost every aspect of the health care delivery system. AI has the potential to improve patient safety (e.g., diagnostic accuracy) as well as reduce the burden on clinicians (e.g., documentation-related workload); however, these benefits are yet to be realized. AI is only one element of a larger sociotechnical system that needs to be considered for effective AI application. In this paper, we describe the current challenges of integrating AI into clinical care and propose a sociotechnical systems (STS) approach for AI design and implementation. We demonstrate the importance of an STS approach through a case study on the design and implementation of a clinical decision support (CDS). In order for AI to reach its potential, the entire work system as well as clinical workflow must be systematically considered throughout the design of AI technology.
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“At the sociotechnical level, the framework treats the clinician-AI diagnostic interaction as a system in which computational tools, institutional workflows, regulatory requirements, and human reasoning are mutually constitutive”
From A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification (2026) · cites this paper as Salwei and Carayon, 2022
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“The integration of artificial intelligence (AI) in health care settings demands a nuanced approach that considers both technical performance and sociotechnical factors.”
From Checklist Approach to Developing and Implementing AI in Clinical Settings: Instrument Development Study (2025) · cites this paper as [1]
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“Their model demonstrates that an AI can only integrate into clinical workflows if it fits within the context, or the work system, in which it is implemented.”
From Checklist Approach to Developing and Implementing AI in Clinical Settings: Instrument Development Study (2025) · cites this paper as [1]
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“Salwei and Carayon [1] developed a sociotechnical systems framework for AI that acknowledges the social and technical aspects of work that relate to the successful design and implementation of AI.”
From Checklist Approach to Developing and Implementing AI in Clinical Settings: Instrument Development Study (2025) · cites this paper as [1]
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Citations
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“Early in the development of AI technologies, designers need to consider how feedback loops will be integrated in the work system to support the monitoring of the technology in real use and to help identify problems as they arise (Carayon et al., 2006, 2014).”
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Leveraging AI to Accelerate Competency-Based Education in the Health Professions
Lomis, K. D., Pusic, M. V., Warm, E. J., Busari, J. O., Huwendiek, S., Incoll, I. W., Lau, S. T., Boscardin, C., Chen, C., Tanaka, P. P., et al. (2026) · Perspectives on Medical Education · doi:10.5334/pme.2594
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AI in Long-Term Care: A Closed-Loop Sociotechnical Reliability Framework
Min, K., & Min, J. (2026) · Journal of Medical Internet Research · doi:10.2196/105161
Metadata
- Authors
- Salwei, M. E., & Carayon, P.
- Affiliations
- Vanderbilt University Medical Center (US)
- University of Wisconsin–Madison (US)
- Corresponding author
- Salwei, Megan E.
- Venue
- Journal of Cognitive Engineering and Decision Making
- Publisher
- SAGE Publications
- Published
- 2022-05-11
- Volume
- 16
- Issue
- 4
- Pages
- 194-206
- ISSN
- 1555-3434 (ISSN-L)
- PMC
- PMC9873227
- OpenAlex
- W4280579451
- Cited by
- 112
- References
- 56
- Citations by year
- 2026: 58 · 2025: 29 · 2024: 18 · 2023: 5 · 2022: 2
- FWCI
- 4.07
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Cognitive Engineering and Decision Making (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Center for Advancing Translational Sciences (1UL1TR002373)
- Agency for Healthcare Research and Quality (K12HS026395, R01HS022086)
- U.S. National Library of Medicine (T15LM007450-19)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems Theories and Implementation
- Keywords
- artificial intelligence in healthcare, socio-technical systems, clinical decision support, clinical workflow, patient safety, clinician workload, work system design, AI implementation
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